Triple
T27762338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batch Normalization |
E701500
|
entity |
| Predicate | normalizesTo |
P162707
|
FINISHED |
| Object | unit variance |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: unit variance | Statement: [Batch Normalization, normalizesTo, unit variance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: normalizesTo Context triple: [Batch Normalization, normalizesTo, unit variance]
-
A.
oftenNormalizedTo
Indicates that one entity is frequently converted, mapped, or standardized into the form or representation of another entity.
-
B.
usesNormalization
Indicates that one entity applies or relies on a normalization process or technique in relation to another entity or data.
-
C.
haveNormalization
chosen
Indicates that one entity serves as a normalization or standardized form of another entity.
-
D.
normalizationProperty
Indicates that one entity specifies a rule, status, or characteristic governing how another entity is normalized or brought into a standard form.
-
E.
refinesNormalization
Indicates that one normalization process or scheme improves, clarifies, or makes more precise another existing normalization.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f63894e5848190aec428392562ab06 |
completed | May 2, 2026, 5:47 p.m. |
| PD | Predicate disambiguation | batch_69f6370c8c7c8190a02ea82847bb6e76 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 4:28 p.m.